The Liston laboratory works on regulatory T cells. These are a type of white blood cell that act to suppress the rest of the immune response, preventing spontaneous autoimmune disease and acting as a rheostat to control just how active our immune system is. The number of these cells in our blood goes up as we get old, which may contribute to the immune-suppressed state of older persons. We seek to understand these cells, using both patient material and mouse models, so that we can harness their power to fine-tune the immune system for healthy ageing.
Severe congenital neutropenia presents with recurrent infections early in life due to arrested granulopoiesis. Multiple genetic defects are known to block granulocyte differentiation, however a genetic cause remains unknown in approximately 40% of cases.
The advent of high-dimensional single-cell data has necessitated the development of dimensionality-reduction tools. t-Distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are the two most frequently used approaches, allowing clear visualization of complex single-cell datasets. Despite the need for quantitative comparison, t-SNE and UMAP have largely remained visualization tools due to the lack of robust statistical approaches. Here, we have derived a statistical test for evaluating the difference between dimensionality-reduced datasets using the Kolmogorov-Smirnov test on the distributions of cross entropy of single cells within each dataset. As the approach uses the inter-relationship of single cells for comparison, the resulting statistic is robust and capable of identifying true biological variation. Further, the test provides a valid distance between single-cell datasets, allowing the organization of multiple samples into a dendrogram for quantitative comparison of complex datasets. These results demonstrate the largely untapped potential of dimensionality-reduction tools for biomedical data analysis beyond visualization.
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